Bridging the AI Talent Gap

Currently, during the campus recruitment season, many enterprises express a strong demand for talent in artificial intelligence (AI) and big data. The cultivation of AI talent is not only an urgent need for industrial transformation and upgrading but also effectively connects the innovation chain, industrial chain, and talent chain, injecting strong momentum into the integrated development of educational and technological talents.

In recent years, China has made significant progress in AI talent cultivation, forming a collaborative education model among government, schools, and enterprises. Various regions and departments have adopted diverse practices. For instance, Guangdong Province has launched a “2+1” program for AI education in primary and secondary schools, while Shenzhen Polytechnic has partnered with Huawei to establish an AI technology industry college, creating a unique model of “industry demand + technical breakthroughs”. In Jiangxi Province, 31 undergraduate institutions have introduced AI-related majors, establishing five provincial-level modern industry colleges, with eight majors recognized as national first-class undergraduate programs, achieving precise alignment between talent supply and regional industrial needs. Liaoning Province has implemented the “Skills Empower Enterprises” initiative, planning to establish three to five provincial-level high-skill talent bases in the AI field, training over 30,000 technical personnel annually. Statistics show that more than 600 undergraduate colleges and over 2,200 vocational colleges across the country now offer AI-related programs, with both the scale and quality of talent cultivation improving simultaneously. Additionally, a series of policies, including the “New Generation AI Development Plan” and “Opinions on Deepening Industry-Education Integration”, have established strategic positioning for AI talent cultivation, built a framework for school-enterprise collaborative education, and detailed the pathways for talent development across all educational stages.

AI talent cultivation has become a core arena for strategic competition among countries. The United States adopts a model of “full-stage penetration + interdisciplinary integration + market-driven” approach, integrating AI education throughout all educational stages. Institutions like Stanford University and MIT have established interdisciplinary AI research institutes, with companies like Google and Microsoft deeply involved in curriculum design and laboratory construction, achieving seamless connections between market demands and academic innovation through problem-oriented project-based learning. Germany, on the other hand, focuses on a “dual system” tradition, constructing a dual-track system of “theoretical teaching in universities + practical training in enterprises”, incentivizing corporate participation through policy subsidies. Companies like Siemens and Bosch collaborate with universities to set standards and develop curricula, ensuring that the talent cultivated meets the demands of “Industry 4.0”.

In China, however, there are still several issues that need to be addressed in AI talent cultivation. For example, there is a mismatch between supply and demand, with curriculum systems lagging behind the iterations of technologies such as large models and multimodal systems. There is a disconnect between theoretical teaching and practical applications in enterprises, and the supply of interdisciplinary talents does not match the needs of industrial upgrades. Additionally, barriers between disciplines have not been broken, with insufficient integration of AI with mathematics, computer science, and biology, making it difficult to cultivate innovative talents with a multi-disciplinary perspective. Furthermore, the supporting system is weak, with university faculty lacking industry experience and cutting-edge research backgrounds, insufficient incentives for industry experts to participate in teaching, and shortages of training platforms, computing resources, and real-world scenarios. Talent evaluation often prioritizes publications over practical experience, and there is a lack of smooth transitions across educational stages, with weak AI enlightenment in primary and secondary education and inadequate early training mechanisms for top talents. Addressing these issues requires collaborative efforts from the government, universities, and enterprises to bridge the AI talent gap.

Strengthening overall coordination and solidifying institutional foundations is essential. AI talent cultivation should be included in national and local special plans, improving the collaborative mechanisms among education, technology, and industry departments to align industrial demands with educational resources. Enterprises that deeply engage in industry-education integration should be granted tax incentives and research subsidies. A special fund for AI talent cultivation should be established to support the co-construction of interdisciplinary platforms and training bases between schools and enterprises. Accelerating the construction of talent evaluation and certification systems, formulating standards for AI talent capabilities, and integrating ethical governance into the entire cultivation process are also crucial.

Deepening teaching reforms and solidifying the educational foundation is vital. Breaking down departmental barriers, constructing interdisciplinary research institutes such as “AI + Manufacturing” and “AI + Healthcare”, and promoting seamless training from undergraduate to doctoral levels are necessary steps. Adding cutting-edge courses on large model applications and multimodal interactions, developing dynamic “living textbooks”, and ensuring that teaching evolves in sync with technological advancements are essential. Enhancing school-enterprise collaboration by integrating industrial scenarios and research projects into teaching and co-building shared laboratories and computing platforms is also important. Optimizing evaluation orientations by reducing the weight of academic publications and incorporating practical achievements in technology transfer and industry services as core evaluation indicators for faculty and students is needed.

Enhancing the role of enterprises and strengthening industrial support are crucial. Talent cultivation should be integrated into development strategies, with full participation in the formulation of training programs and curriculum design, pushing corporate standards and job competency requirements into the classroom. Enterprises should provide access to computing resources, application scenarios, and anonymized data to universities, co-establish joint research centers, and conduct project-based and problem-solving education around technical challenges. Improving talent incentive pathways by establishing direct internship and employment programs, youth AI talent support plans, and achievement transformation reward mechanisms will create a sustainable ecosystem for talent cultivation, utilization, and development.

The competition in AI is fundamentally a competition for talent. By focusing on AI talent cultivation and collaboratively promoting the integrated development of educational and technological talents, China can gain strategic advantages and contribute significantly to its position in the new round of global technological competition.